Article: Smart contract and distributed ledger based financial transaction settlement and tracking model design in digital economy Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2026 Vol.30 No.7 pp.133 - 149 Abstract: With the rapid development of financial technology and the digital economy, fraud detection in financial transactions faces increasing challenges due to complex transaction networks, temporal dependencies, and nonlinear interactions. This study proposes an RL-LGNN framework that integrates long short-term memory (LSTM) networks, graph neural networks (GNN), and reinforcement learning (RL) for fraud detection in the financial transaction settlement process. LSTM is used to encode historical transactions as temporal sequences and extract time-dependent behavioural features. GNN then models inter-node transaction relationships and captures structural information from the transaction graph. On this basis, RL is introduced to dynamically optimise the detection strategy, thereby improving model adaptability and robustness. Experimental results on both public and real-world datasets show that the proposed framework outperforms conventional methods and achieves fraud detection accuracy above 90%. The proposed framework provides an effective solution for fraud detection in financial transaction settlement. Inderscience Publishers - linking academia, business and industry through research

Title: Smart contract and distributed ledger based financial transaction settlement and tracking model design in digital economy

Authors: Wei Zhong

Addresses: Ancheng Property & Casualty Insurance Co., Ltd., Chongqing, 400010, China

Abstract: With the rapid development of financial technology and the digital economy, fraud detection in financial transactions faces increasing challenges due to complex transaction networks, temporal dependencies, and nonlinear interactions. This study proposes an RL-LGNN framework that integrates long short-term memory (LSTM) networks, graph neural networks (GNN), and reinforcement learning (RL) for fraud detection in the financial transaction settlement process. LSTM is used to encode historical transactions as temporal sequences and extract time-dependent behavioural features. GNN then models inter-node transaction relationships and captures structural information from the transaction graph. On this basis, RL is introduced to dynamically optimise the detection strategy, thereby improving model adaptability and robustness. Experimental results on both public and real-world datasets show that the proposed framework outperforms conventional methods and achieves fraud detection accuracy above 90%. The proposed framework provides an effective solution for fraud detection in financial transaction settlement.

Keywords: financial transaction; fraud detection; graph neural network; GNN; reinforcement learning.

DOI: 10.1504/IJDMB.2026.154763

International Journal of Data Mining and Bioinformatics, 2026 Vol.30 No.7, pp.133 - 149

Received: 17 Mar 2026
Accepted: 15 May 2026
Published online: 13 Jul 2026 *

Title: Smart contract and distributed ledger based financial transaction settlement and tracking model design in digital economy

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